Düsseldorf, University, Diss., 2003.

Ulm, Univ., Habil.-Schr., 1997. / http://vts.uni-ulm.de/query/longview.meta.asp?documentid=382.

Bielefeld, University, Diss., 2000.

Diese Arbeit zeigt das Vorkommen von fünfundzwanzig neuen Norovirusstämmen in Deutschland und stellt ihre phylogenetischen Verwandschaftsverhältnisse dar. Bei zweien dieser Stämme handelte es sich um natürlich entstandene rekombinante Viren, deren Rekombinationsbruchpunkte innerhalb der Capsidregion lagen. Die Schnittstellen wiesen in ihrer unmittelbaren Umgebung charakteristische Eigenschaften rekombinanter Viren auf. Die Virulenz und die Antigenität des Erregers, sowie die Methoden der taxonomischen Zuordnung wurden in Bezug auf die Tragweite dieser Ergebnisse diskutiert. Die genetische Information für den Virusnachweis wurde aus 119 NV-positiven Stuhlproben von bis zu vier Jahre alten Kindern isoliert und sind in den Städten Hamburg, Bochum, Freiburg, Erlangen und Dresden zwischen 1997 und 1998 gesammelt worden. Durch Amplifikation und Sequenzierung wurde die komplette Capsidsequenz von fünfundzwanzig zuvor noch nicht beschriebenen NV-Stämmen ermittelt und mit Maximum-Likelihood Analysen ihre verwandschaftliche Beziehung als phylogenetischer Baum dargestellt. Durch verschiedene, zum Rekombinationsnachweis geeignete Methoden (Exploratory Tree Analysis, Similarity Plots, Splits Tree und Sawyer´s Test) wurden der Datensatz auf das Vorkommen von Rekombinationsereignissen untersucht. Splits Tree lieferte zunächst Hinweise auf insgesamt drei rekombinante Stämme: Hamburg180/1997/GE (HH180), Hamburg137/1997/GE (HH137) und Bochum272/1987/GE (BO272). Durch die im Anschluss verwendeten Methoden (s. o.) wurden jedoch nur die zwei Erstgenannten als Mosaiksequenzen bestätigt, so dass der Stamm BO272 nicht in die Endergebnisse aufgenommen wurde. Sim Plot und Exploratory Tree Analysis ordneten den rekombinanten Stämmen die jeweiligen Parentalstämme zu. Dem zufolge entstanden beide Rekombinanten aus Viren der NV-Genogruppe II/4, HH180 aus Hamburg189/1997/GE und Bochum024/1998/GE und HH137 aus Hamburg189/1997/GE und Hamburg139/1997/GE. Durch das Programm LARD wurden die genauen Lokalisationen der jeweiligen Rekombinationsbruchpunkte ermittelt. Diese befanden sich beim Stamm HH180 an den Sequenzpositionen 519 nt und 762 nt und beim Stamm HH137 an der Position 768 nt. Vor und nach diesen Bruchpunkten wurden Maximum-Likelihood-Bäume aus dem rekombinantem Stamm, den beiden Parentalstämmen und einer Außengruppe erstellt. Mit hohen Boostrapwerten für die einzelnen Verzweigungen wechselten beide rekombinanten Stämme nach jedem Bruchpunkt ihre phylogenetische Zugehörigkeit zu einem anderen Parentalvirus innerhalb des konstruierten Baumes. Um beurteilen zu können, ob Noroviren generell dazu neigen, genetische Information auszutauschen, wurde die Struktur der Rekombinationsregion anaysiert und mit der Struktur von Viren, bei denen Rekombinationsereignisse gehäuft nachgewiesen werden konnten, verglichen. Beide Stämmme zeigten typische Eigenschaften von sogenannten ‚homologous recombination activators’. In Bezug auf die Genomorganisation befanden sich bei jeder Rekombinanten eine der Schnittstellen im Bereich der Protruding-Region der Capsidsequenz, in der sich vermutlich die Antikörper-Bindungsstelle des Virus befindet. Diese Studie zeigt also im Einklang mit späteren Ergebnissen, dass homologe Rekombination innerhalb des NV-Capsidgens kein isoliertes Ereignis darstellt. Im Hinblick auf immunologische Bedeutung und Klassifikation wurde die Tragweite von Rekombinationsereignissen innerhalb verschiedener Genomabschnitten diskutiert und alternative Lösungen vorgeschlagen. Es ist fraglich, ob bei natürlichem Vorkommen von Rekombination in wahrscheinlich immunologisch bedeutsamen Regionen des Virusgenoms die Sequenzierung der Polymerase-Region zur Klassifikation ausreicht, oder ob die Verwendung der Capsidregion sinnvoller wäre. / In this study we have assessed the occurence of recombination in the norovirus capsid gene. For this purpose, 25 complete capsid sequences of norovirus strains, generated from norovirus-positive clinical samples were examined. Recombination was detected in two of the 25 strains, both belonging to the genetic cluster II/4 and so did the parental strains, which were also identified. Recombination breakpoints were in one of the strains located at the interface P1-1 and P2 domain, and in the other, there was an additional breakpoint in the S domain. Each one was validated independently by different recombination detecting methods (SimPlot, Exploratory Tree Analysis, Sawyer´s Test, and LARD). The recombination region displayed features such as length, sequence composition, and predicted RNA secondary structure that are characteristic of homologous recombination activators. Our results suggest that recombination in the norovirus capsid gene may naturally occur, involving capsid domains presumably exposed to immunological pressure.

Genealogy reconstruction is widely used in biology when relationships among entities are studied. Phylogenies, or evolutionary trees, show the differences between species. They are of profound importance because they help to obtain better understandings of evolutionary processes. Pedigrees, or family trees, on the other hand visualize the relatedness between individuals in a population. The reconstruction of pedigrees and the inference of parentage in general is now a cornerstone in molecular ecology. Applications include the direct infer- ence of gene flow, estimation of the effective population size and parameters describing the population’s mating behaviour such as rates of inbreeding. In the first part of this thesis, we construct genealogies of various types of cancer. Histopatho- logical classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. We introduce a novel algorithm to rank tumor subtypes according to the dis- similarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia and liposarcoma subtypes and then apply it to a broader group of sarcomas and of breast cancer subtypes. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors. In contrast to asexually reproducing cancer cell populations, pedigrees of sexually reproduc- ing populations cannot be represented by phylogenetic trees. Pedigrees are directed acyclic graphs (DAGs) and therefore resemble more phylogenetic networks where reticulate events are indicated by vertices with two incoming arcs. We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatel- lites and single nucleotide polymorphism (SNPs) in the second part of the thesis. If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical data set with known pedigree. The parentage inference is robust even in the presence of genotyping errors. We further demonstrate the accuracy of the algorithm on simulated clonal populations. We show that the joint estimation of parameters of inter- est such as the rate of self-fertilization or clonality is possible with high accuracy even with marker panels of moderate power. Classical methods can only assign a very limited number of statistically significant parentages in this case and would therefore fail. The method is implemented in a fast and easy to use open source software that scales to large datasets with many thousand individuals.

Kulturunabhängige 16S rRNA Analyse des subgingivalen bakteriellen Biofilms bei der aggressiven Parodontitis und Vergleich mit bekannten Bakterien bzw. Phylotypen, die im Zusammenhang mit der parodontalen Flora nachgewiesen wurde. Putative Pathogene wurden bestimmt. / In this culture independent 16S rRNA study cloning and sequencing was used to analyse gingival samples from a population of 26 persons suffering from aggressive periodontitis and six healthy adult individuals.

Genealogy reconstruction is widely used in biology when relationships among entities are studied. Phylogenies, or evolutionary trees, show the differences between species. They are of profound importance because they help to obtain better understandings of evolutionary processes. Pedigrees, or family trees, on the other hand visualize the relatedness between individuals in a population. The reconstruction of pedigrees and the inference of parentage in general is now a cornerstone in molecular ecology. Applications include the direct infer- ence of gene flow, estimation of the effective population size and parameters describing the population’s mating behaviour such as rates of inbreeding. In the first part of this thesis, we construct genealogies of various types of cancer. Histopatho- logical classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. We introduce a novel algorithm to rank tumor subtypes according to the dis- similarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia and liposarcoma subtypes and then apply it to a broader group of sarcomas and of breast cancer subtypes. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors. In contrast to asexually reproducing cancer cell populations, pedigrees of sexually reproduc- ing populations cannot be represented by phylogenetic trees. Pedigrees are directed acyclic graphs (DAGs) and therefore resemble more phylogenetic networks where reticulate events are indicated by vertices with two incoming arcs. We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatel- lites and single nucleotide polymorphism (SNPs) in the second part of the thesis. If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical data set with known pedigree. The parentage inference is robust even in the presence of genotyping errors. We further demonstrate the accuracy of the algorithm on simulated clonal populations. We show that the joint estimation of parameters of inter- est such as the rate of self-fertilization or clonality is possible with high accuracy even with marker panels of moderate power. Classical methods can only assign a very limited number of statistically significant parentages in this case and would therefore fail. The method is implemented in a fast and easy to use open source software that scales to large datasets with many thousand individuals.:Abstract v Acknowledgments vii 1 Introduction 1 2 Cancer Phylogenies 7 2.1 Introduction..................................... 7 2.2 Background..................................... 9 2.2.1 PhylogeneticTrees............................. 9 2.2.2 Microarrays................................. 10 2.3 Methods....................................... 11 2.3.1 Datasetcompilation ............................ 11 2.3.2 Statistical Methods and Analysis..................... 13 2.3.3 Comparison of our methodology to other methods . . . . . . . . . . . 15 2.4 Results........................................ 16 2.4.1 Phylogenetic tree reconstruction method. . . . . . . . . . . . . . . . . 16 2.4.2 Comparison of tree reconstruction methods to other algorithms . . . . 28 2.4.3 Systematic analysis of methods and parameters . . . . . . . . . . . . . 30 2.5 Discussion...................................... 32 3 Wild Pedigrees 35 3.1 Introduction..................................... 35 3.2 The molecular ecologist’s tools of the trade ................... 36 3.2.1 3.2.2 3.2.3 3.2.1 Sibship inference and parental reconstruction . . . . . . . . . . . . . . 37 3.2.2 Parentage and paternity inference .................... 39 3.2.3 Multigenerational pedigree reconstruction . . . . . . . . . . . . . . . . 40 3.3 Background..................................... 40 3.3.1 Pedigrees .................................. 40 3.3.2 Genotypes.................................. 41 3.3.3 Mendelian segregation probability .................... 41 3.3.4 LOD Scores................................. 43 3.3.5 Genotyping Errors ............................. 43 3.3.6 IBD coefficients............................... 45 3.3.7 Bayesian MCMC.............................. 46 3.4 Methods....................................... 47 3.4.1 Likelihood Model.............................. 47 3.4.2 Efficient Likelihood Calculation...................... 49 3.4.3 Maximum Likelihood Pedigree ...................... 51 3.4.4 Full siblings................................. 52 3.4.5 Algorithm.................................. 53 3.4.6 Missing Values ............................... 56 3.4.7 Allelefrequencies.............................. 58 3.4.8 Rates of Self-fertilization.......................... 60 3.4.9 Rates of Clonality ............................. 60 3.5 Results........................................ 61 3.5.1 Real Microsatellite Data.......................... 61 3.5.2 Simulated Human Population....................... 62 3.5.3 SimulatedClonalPlantPopulation.................... 64 3.6 Discussion...................................... 71 4 Conclusions 77 A FRANz 79 A.1 Availability ..................................... 79 A.2 Input files...................................... 79 A.2.1 Maininputfile ............................... 79 A.2.2 Knownrelationships ............................ 80 A.2.3 Allele frequencies.............................. 81 A.2.4 Sampling locations............................. 82 A.3 Output files..................................... 83 A.4 Web 2.0 Interface.................................. 86 List of Figures 87 List of Tables 88 List Abbreviations 90 Bibliography 92 Curriculum Vitae I